arrow
返回

Towards quantum computing based community detection

delete2020-11-01
delete12
PRE
AI
S
Sana Akbar *
S
Sri Khetwat Saritha
DOI:10.1016/j.cosrev.2020.100313delete
delete原文链接
delete原文求助
delete分享
delete收藏
摘要

摘要

En 中文
Over the past decade, social network analysis has earned pivotal eminence in the area of web mining and information retrieval. Community detection, being the indispensable part of social network analysis; has garnered far reaching usance in business analytics, healthcare, security, research and policy making. With the embodiment of copious domains to social networks and unprecedented rise in the data - produced, accessed and stored globally; the task of handling the unpredictable, dynamic and ever evolving topological nature of social networks has become arduous. In this regard, quantum computing (QC) has emerged as the most promising trailblazer guaranteeing unprecedented data storage and manipulation capabilities by- dynamic allocation of cluster size and architecture, quantum parallelism, reduced parameter dependency, etc. QC based algorithms have registered exponential speedup over many classical problems with better efficiency and abated time complexity; apart from solving NP-hard problems that were unrealizable classically. Accordingly, a comprehensive literature survey has been presented for social network analysis and community detection highlighting the limitations prevalent in the current technologies. A brief insight into quantum computing and its proficiency in rendering to larger storage systems has been presented; as a solution to the inherent problems present in the existing community detection approaches. A systematic account of quantum computing based community detection techniques has been summarized and discussed as a more prudent future alternative to social network analysis and community detection. Lastly, complexity analysis and modularity based comparison of QC based algorithms with other state of the art algorithms has been carried out to establish the supremacy of quantum algorithms in community detection. (C) 2020 Elsevier Inc. All rights reserved.
Keyword:
Social network analysis
Community detection
Quantum computing
Quantum machine learning
Quantum annealing (QA)
AI总结

AI总结

对已上传原文的论文进行重点信息的提取,主要内容包括:简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。

期刊

Computer Science Review 封面图
Computer Science Review
IF:
12.7
论文数:
2.3K
被引数:
5.2K

机构

N
national institute of technology (nit system)
学者数:
4.0W
论文数: 3.7W
被引数: 31